630 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Norway
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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral
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consist of 15 PhDs, that together will work and learn as an international cohort and collaborate across projects to bring together insights anchored in different sectors and countries. The candidates will
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document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the doctoral program from the Faculty of Engineering (https://www.ntnu.edu/studies/phiv ) You
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This
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if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.no/studier/phet) You must
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analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks on material design by using PyTorch or Matlab PLEASE
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https://www.jobbnorge.no/en/available-jobs/job/296771/phd-candidate-in-parasite… Requirements Research FieldBiological sciences » BiologyEducation LevelMaster Degree or equivalent Additional Information
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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affiliated with the Centre, and opportunities for research stays with partners, both nationally and abroad, through its international network. Read more about the Centre and its research at the website: https
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. The project’s PI is Associate Professor Iselin Åsedotter Strønen. For more information about the project, see https://www4.uib.no/en/research/research-projects/invisible-women-of-the-sea-gendering-fishery